LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM

• URL: http://arxiv.org/abs/2305.06404v1
• Date: Wed, 10 May 2023 18:26:42 GMT
• Title: LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM
• Authors: Wen-Yu Hua and Brian Williams and Davood Shamsi
• Abstract summary: We present 8-bit Siamese-BLOOM, a multilingual large language model optimized to produce semantically meaningful word embeddings. We fine-tune BLOOM with a scalable adapter (LoRA) and 8-bit Adam for sentence similarity classification. Experiment results show the quality of learned embeddings from LACoS-BLOOM is proportional to the number of model parameters and the amount of unlabeled training data.
• Score: 2.9327503320877457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text embeddings are useful features for several NLP applications, such as sentence similarity, text clustering, and semantic search. In this paper, we present a Low-rank Adaptation with a Contrastive objective on top of 8-bit Siamese-BLOOM, a multilingual large language model optimized to produce semantically meaningful word embeddings. The innovation is threefold. First, we cast BLOOM weights to 8-bit values. Second, we fine-tune BLOOM with a scalable adapter (LoRA) and 8-bit Adam optimizer for sentence similarity classification. Third, we apply a Siamese architecture on BLOOM model with a contrastive objective to ease the multi-lingual labeled data scarcity. The experiment results show the quality of learned embeddings from LACoS-BLOOM is proportional to the number of model parameters and the amount of unlabeled training data. With the parameter efficient fine-tuning design, we are able to run BLOOM 7.1 billion parameters end-to-end on a single GPU machine with 32GB memory. Compared to previous solution Sentence-BERT, we achieve significant improvement on both English and multi-lingual STS tasks.

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